In recent years, data visualization has become an increasing important part of data analysis. Visualization, for example, enables companies and other organizations to meaningfully present raw data to facilitate effective and efficient analysis of the data. Obtaining and/or processing the raw data to produce a visualization of the data, however, can be a challenge. When visualizing “big data,” for example, the costs related to obtaining and/or processing all of the data may be substantial. While advances in the performance of computer hardware has greatly increased the capabilities of servers and networks to obtain and process data, the amount of data available to be obtained and processed has grown exponentially in comparison to any advances in hardware performance. Although a plan (e.g., a query plan) for obtaining and/or processing data may be optimized for one or more scenarios, it may not be optimal for other scenarios. In addition, a number of variables may affect the speed and/or efficiency of the plan, causing the execution of the plan to be more costly than anticipated. These and other drawbacks exist.